Empirical Validation of RMA Under Synthetic Control Conditions

This study was conducted as a methodological validation/calibration study associated with the development of the 1.4.5 release, following the RMA analyses used in the preceding 1.4.4 release. This dataset documents an empirical validation of Residual Mapping Analysis (RMA) under synthetic control conditions with known ground truth. The study evaluates RMA's ability to discriminate between correctly specified or no-signal data and deliberately misspecified models. Five generative conditions were tested: an external pure-noise control, a correctly specified linear condition, and three deliberately misspecified conditions (nonlinear, composite, and hierarchical). Each condition was evaluated at four sample sizes (n = 51, 100, 300, and 800), using 30 independent random seeds and two targets, yielding 1,200 final executions. Under the tested conditions, correctly specified and no-signal cases produced negative residual R² values that converged toward zero as sample size increased, whereas the misspecified conditions produced positive residual R² values with increasing separation as sample size increased. The pure-noise control produced 0% observed false positives across 240 runs under the framework's default threshold of 0.05. The study provides empirical support for the discriminative behavior of RMA under the tested synthetic conditions. It does not constitute a general validation of RMA across all configurations, nor does it validate downstream findings obtained using RMA in D or ARGIRA. The repository includes the complete raw results, aggregated results, calibration script, block-execution wrapper, frozen experimental protocol, README documentation, and the principal visualization. The final dataset contains 1,200 rows with no duplicate experimental keys and a consistent framework integrity hash across all records. DOI: 10.5281/zenodo.21934823 License: CC BY-NC-SA 4.0